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Artificial intelligence methods in diagnostics of analog systems

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Bilski P., Wojciechowski J.
International Journal of Applied Mathematics and Computer Science
|
2014
|
tom 24
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nr 2
271-282
EN
The paper presents the state of the art and advancement of artificial intelligence methods in analog systems diagnostics. Firstly, the diagnostic domain is introduced and its problems explained. Then, computational intelligence approaches usable for fault detection and identification are reviewed. Particular groups of methods are presented in detail, explaining their usefulness and drawbacks. Examples, such as the induction motor or the electronic filter, are provided to show the applicability of the presented approaches for monitoring the state of analog objects from engineering domains. The discussion section reviews the presented approaches, their future prospects and problems to be solved.
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Automatic parametric fault detection in complex analog systems based on a method of minimum node selection

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Bilski A., Wojciechowski J.
International Journal of Applied Mathematics and Computer Science
|
2016
|
tom 26
|
nr 3
655-668
EN
The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.
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